Visibility Quality Control System of Ground Automatic Station Based on Multi-source Data

Abstract

The present invention discloses a visibility quality control system for a ground automatic station assisted by multi-source data, and specifically relates to the technical field of visibility quality control for ground automatic stations, including: a multi-source data collection module, a ground automatic station meteorological anomaly judgment module, a multi-source data assimilation module and a quality control module, wherein the multi-source data collection module is used to collect various meteorological data from multiple different information sources in real time, the ground automatic station meteorological anomaly judgment module is mainly used to comprehensively check and analyze the meteorological observation data of the ground automatic station to judge whether the meteorology is in a normal state, and to issue an early warning in time when anomalies are found, the multi-source data assimilation module improves the quality and accuracy of meteorological data by integrating meteorological data from different sources, and the quality control module effectively screens and corrects abnormal or suspicious meteorological data by performing spatial consistency checks and background consistency checks, thereby providing high-quality meteorological information.

Description

Technical Field

[0001] The invention belongs to the technical field of ground automatic station visibility quality control, and in particular relates to a visibility quality control system for a ground automatic station assisted by multi-source data. Background Art

[0002] Multi-source data fusion technology is a technology that integrates and processes data from different sources, different formats, and different precisions. Multi-source data fusion technology is widely used to integrate ground automatic station observation data, regional model forecast field data, other meteorological observation data (such as radar, satellite, etc.) and historical data, etc. Through data fusion, the accuracy and reliability of visibility data can be further improved, providing more comprehensive information support for quality control.

[0003] With the continuous development of meteorological observation technology, ground automatic stations have become an important means of meteorological observation in my country. However, due to the influence of various factors such as equipment and environment, the quality of observation data from automatic stations varies, especially visibility data, whose accuracy and stability are of great significance for weather forecasting, climate analysis, and meteorological disaster warning. Therefore, establishing a visibility quality control system for ground automatic stations based on multi-source data assistance plays a vital role in improving the overall quality of meteorological observation data. Summary of the invention

[0004] The purpose of the present invention is to provide a visibility quality control system for ground automatic stations based on multi-source data assistance, which is used to solve the technical problem in the prior art that the ground automatic stations have low visibility due to abnormal meteorological conditions, resulting in uneven observation data quality.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The visibility quality control system of the ground automatic station assisted by multi-source data includes: a multi-source data collection module, a ground automatic station meteorological anomaly judgment module, a multi-source data assimilation module and a quality control module.

[0007] The multi-source data collection module is used to collect meteorological data under different visibility from various information sources in real time. The information sources include: ground automatic stations, radars, satellites, and numerical weather forecast models. The meteorological data include: real-time observation values ​​of temperature, humidity, wind speed, and wind direction at each time point. The time period is divided into time periods with a length of t. The meteorological impact data include temperature state change values, humidity state change values, and wind speed state change values. According to the real-time observation values ​​of meteorological data collected by the ground automatic station, the meteorological impact data of each time period is determined. The specific calculation formula of the temperature state change value is as follows:

[0008] ;

[0009] Among them, x represents the x-th time period, W(x) represents the temperature state change value of the x-th time period, w(x) represents the average temperature of the x-th time period, minw(x) represents the minimum temperature of the x-th time period, maxw(x) represents the maximum temperature of the x-th time period, and the temperature state change value reflects the rising and falling trend of the temperature, which helps to predict the activity of cold and warm air.

[0010] The specific calculation formula for the humidity state change value is as follows:

[0011] ;

[0012] Where x represents the x-th time period, S(x) represents the humidity state change value of the x-th time period, s(x) represents the average humidity value of the x-th time period, a represents a constant, s[x(t)] represents the humidity at the end time point of the x-th time period, and s[x(0)] represents the humidity at the start time point of the x-th time period. The humidity state change value reveals the change in the water vapor content in the air, which helps to improve the accuracy of judging the probability of occurrence of weather phenomena such as rainfall and haze.

[0013] The specific calculation formula for the wind speed state change value is as follows:

[0014] ;

[0015] Among them, x represents the x-th time period, F(x) represents the wind speed state change value in the x-th time period, f(x) represents the average wind speed in the x-th time period, p(x) represents the frequency of wind direction blowing towards the ground automatic station in the x-th time period, and the wind speed state change value is directly related to the change in wind strength within the data collection range of the ground automatic station.

[0016] The ground automatic station meteorological anomaly judgment module performs status checks on the ground automatic station and meteorological impact data. The status checks specifically include the following:

[0017] Sensor status check: judging the working status of the sensor equipment based on the switch status and voltage status of each sensor of the ground automatic station. The switch status includes the on state and the off state. Based on the preset normal voltage range of each sensor, when the sensor is in the on state and the working voltage is in the normal voltage range, it means that the sensor status check of the sensor has passed. Otherwise, it means that the sensor status check of the sensor has failed.

[0018] Data status check: according to the format and method requirements of the data collection by the relevant organizations, such as the format requirements of the meteorological bureau for the data files of ground automatic stations, check the wrong format and missing parts in the collected data, and correct the wrong format and missing parts, such as modifying the length of the data record to make the data meet the format requirements, replacing and deleting the missing parts based on the historical data, checking the internal comparison relationship and time sequence of the data based on the basic knowledge of meteorological observation, recording the data that does not meet the normal comparison relationship and time sequence as wrong data, and adjusting the wrong data based on the basic knowledge of meteorological observation;

[0019] Suspicious data check: according to the historical data collected by the ground automatic station under normal conditions and the climatological laws, set the limit values ​​for the data in different seasons and time periods. For example, the minimum limit value of the temperature near the ground automatic station K at noon in summer is 15 degrees Celsius, and the maximum limit value is 40 degrees Celsius. When the data at a certain time point exceeds the limit value, the data in this period is considered suspicious, recorded as suspicious data, and all suspicious data are deleted;

[0020] By processing and analyzing the real-time meteorological impact data, it is determined whether the meteorological conditions in the observation environment of the ground automatic station are normal. The meteorological impact index is set based on the comprehensive meteorological impact data. The specific formula of the meteorological impact index is as follows:

[0021] ;

[0022] Where x represents the xth time period, Q(x) represents the meteorological impact index of the xth time period, W(x) represents the temperature state change value of the xth time period, S(x) represents the humidity state change value of the xth time period, and F(x) represents the wind speed state change value of the xth time period. The weight coefficient representing the change value of the temperature state, The weight coefficient representing the humidity state change value, The weight coefficient representing the wind speed state change value.

[0023] The visibility threshold is determined according to the actual requirements for the visibility of the ground automatic station. Based on the historical data of the ground automatic station, the time period when the visibility of the ground automatic station is less than or equal to the visibility threshold is determined, which is recorded as the meteorological anomaly time period. The meteorological impact data within the meteorological anomaly time period is further obtained, and the meteorological impact index within the meteorological anomaly time period is calculated. The normal meteorological impact index range is determined based on the meteorological impact index within the meteorological anomaly time period, and the meteorological impact index after the status check is calculated. If the meteorological impact index exceeds the normal meteorological impact index range, the module will trigger the corresponding warning signal and output the meteorological anomaly report to the multi-source data assimilation module.

[0024] The multi-source data assimilation module is set in the Rth time period. At the time point when the meteorological anomaly report is received, the multi-source data is assimilated. The meteorological data forecast value output by the numerical weather forecast model is compared with the meteorological data observation value of the ground automatic station. The accuracy of the observation data is improved through data assimilation. The meteorological data forecast value, meteorological data observation value and meteorological data error are combined through the three-dimensional variational assimilation method to obtain the meteorological data that best conforms to the actual meteorological state, which is recorded as the meteorological data analysis value. The specific formula of the meteorological data analysis value is as follows:

[0025] ;

[0026] Where i represents the i-th time point, which is within the time period R, and a(i) represents the meteorological data analysis value at the i-th time point. represents the forecast value of meteorological data at the i-th time point, represents the observed value of meteorological data at the i-th time point, represents the inverse matrix of the meteorological data forecast error covariance matrix, which is obtained from the numerical weather forecast model parameters of the output meteorological data forecast value. H represents the observation operator, which is used to map the model variables from the grid space to the observation space. T represents the transposition sign, r represents the observation error, and u represents the representative error. Express The model variables are used in the grid space to obtain the theoretical values ​​at the observation points.

[0027] The quality control module implements the quality control of the visibility of the ground automatic station by performing spatial consistency check and background consistency check on the meteorological data observation values. The spatial consistency check needs to compare the data of the meteorological data observation values ​​of the checked point with those of the surrounding observation points, and adopts objective analysis methods, such as interpolation method and Barnes method. The estimated value of the checked point is calculated based on the data of the surrounding observation points. The thresholds of different data types are set according to the characteristics of the data and application requirements. The data types are distinguished by temperature, humidity, wind speed and direction. The difference between the meteorological data observation value and the forecast value of the checked point is compared with the size relationship of the given threshold. If the difference between the meteorological data observation value and the forecast value of the checked point is greater than the threshold, the meteorological data analysis value is considered to be abnormal or suspicious and has not passed the spatial consistency check. Otherwise, the meteorological data observation value is judged to have passed the spatial consistency check, thereby screening the meteorological data analysis values ​​that meet the quality requirements;

[0028] The representative error is processed by background consistency check. Within the time period R, the difference ax(i) between the observed value and the forecast value of the meteorological data at each time point and the average value of the difference are calculated. The standard deviation of the observed and predicted values ​​of the meteorological data for this period , set the meteorological data evaluation index, the specific formula is as follows:

[0029] ;

[0030] A threshold value V of the evaluation index is preset. When Pj(i) is greater than or equal to the threshold value, the observation value and forecast value of the meteorological data at that time point are removed, thereby removing the analysis value of the meteorological data at that time point, wherein the threshold value V is determined by a double weight method.

[0031] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0032] 1. The present invention introduces meteorological observation data from other sources, compares and verifies the observation data from ground automatic stations, sets meteorological impact indicators, and uses automated and intelligent technical means to achieve real-time processing and analysis of meteorological observation data, which helps to timely discover and correct errors in ground automatic station data, and provides strong support for optimizing meteorological resource allocation by accurately judging abnormal situations in the meteorological observation environment;

[0033] 2. The present invention uses a three-dimensional variational assimilation method to combine meteorological data forecast values, observation values ​​and their respective errors, taking into account not only the errors of the observation data but also the errors of the model forecast. The generated meteorological data analysis values ​​have higher accuracy and reliability, thus being able to more accurately reflect the true situation of the meteorological state;

[0034] 3. The present invention compares the checkpoint data with the observation point data through spatial consistency check, identifies and corrects the errors in the checkpoint data, thereby improving the accuracy of the data. Through background consistency check, the difference between the observed value and the forecast value and their stability are evaluated, and abnormal data are further eliminated. In the case of low visibility at the ground automatic station due to abnormal meteorological conditions, accurate meteorological data analysis values ​​are determined with the assistance of multi-source data, thereby achieving quality control of the visibility of the ground automatic station. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 The module diagram of the visibility quality control system of ground automatic station assisted by multi-source data is shown;

[0037] Figure 2 The following is a step diagram of the method for quality control of visibility of ground automatic stations assisted by multi-source data;

[0038] Figure 3 A method step diagram for setting meteorological impact indicators is shown. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] like Figure 1 , Figure 2 , Figure 3 The visibility quality control system based on multi-source data-assisted ground automatic station shown in the figure specifically includes the following contents:

[0041] The multi-source data collection module is used to collect meteorological data under different visibility from various information sources in real time. The information sources include: ground automatic stations, radars, satellites, and numerical weather forecast models. The meteorological data include: real-time observation values ​​of temperature, humidity, wind speed, and wind direction at each time point. The time period is divided into time periods with a length of t. The meteorological impact data include temperature state change values, humidity state change values, and wind speed state change values. According to the real-time observation values ​​of meteorological data collected by the ground automatic station, the meteorological impact data of each time period is determined. The specific calculation formula of the temperature state change value is as follows:

[0042] ;

[0043] Among them, x represents the x-th time period, W(x) represents the temperature state change value of the x-th time period, w(x) represents the average temperature of the x-th time period, minw(x) represents the minimum temperature of the x-th time period, maxw(x) represents the maximum temperature of the x-th time period, and the temperature state change value reflects the rising and falling trend of the temperature, which helps to predict the activity of cold and warm air.

[0044] The specific calculation formula for the humidity state change value is as follows:

[0045] ;

[0046] Where x represents the x-th time period, S(x) represents the humidity state change value of the x-th time period, s(x) represents the average humidity value of the x-th time period, a represents a constant, s[x(t)] represents the humidity at the end time point of the x-th time period, and s[x(0)] represents the humidity at the start time point of the x-th time period. The humidity state change value reveals the change in the water vapor content in the air, which helps to improve the accuracy of judging the probability of occurrence of weather phenomena such as rainfall and haze.

[0047] The specific calculation formula for the wind speed state change value is as follows:

[0048] ;

[0049] Among them, x represents the x-th time period, F(x) represents the wind speed state change value in the x-th time period, f(x) represents the average wind speed in the x-th time period, p(x) represents the frequency of wind direction blowing towards the ground automatic station in the x-th time period, and the wind speed state change value is directly related to the change in wind strength within the data collection range of the ground automatic station.

[0050] The ground automatic station meteorological anomaly judgment module performs status checks on the ground automatic station and meteorological impact data. The status checks specifically include the following:

[0051] Sensor status check: judging the working status of the sensor equipment based on the switch status and voltage status of each sensor of the ground automatic station. The switch status includes the on state and the off state. Based on the preset normal voltage range of each sensor, when the sensor is in the on state and the working voltage is in the normal voltage range, it means that the sensor status check of the sensor has passed. Otherwise, it means that the sensor status check of the sensor has failed.

[0052] Data status check: according to the format and method requirements of the data collection by the relevant organizations, such as the format requirements of the meteorological bureau for the data files of ground automatic stations, check the wrong format and missing parts in the collected data, and correct the wrong format and missing parts, such as modifying the length of the data record to make the data meet the format requirements, replacing and deleting the missing parts based on the historical data, checking the internal comparison relationship and time sequence of the data based on the basic knowledge of meteorological observation, recording the data that does not meet the normal comparison relationship and time sequence as wrong data, and adjusting the wrong data based on the basic knowledge of meteorological observation;

[0053] Suspicious data check: according to the historical data collected by the ground automatic station under normal conditions and the climatological laws, set the limit values ​​for the data in different seasons and time periods. For example, the minimum limit value of the temperature near the ground automatic station K at noon in summer is 15 degrees Celsius, and the maximum limit value is 40 degrees Celsius. When the data at a certain time point exceeds the limit value, the data in this period is considered suspicious, recorded as suspicious data, and all suspicious data are deleted;

[0054] By processing and analyzing the real-time meteorological impact data, it is determined whether the meteorological conditions in the observation environment of the ground automatic station are normal. The meteorological impact index is set based on the comprehensive meteorological impact data. The specific formula of the meteorological impact index is as follows:

[0055] ;

[0056] Where x represents the xth time period, Q(x) represents the meteorological impact index of the xth time period, W(x) represents the temperature state change value of the xth time period, S(x) represents the humidity state change value of the xth time period, and F(x) represents the wind speed state change value of the xth time period. The weight coefficient representing the change value of the temperature state, The weight coefficient representing the humidity state change value, The weight coefficient representing the wind speed state change value.

[0057] The visibility threshold is determined according to the actual requirements for the visibility of the ground automatic station. Based on the historical data of the ground automatic station, the time period when the visibility of the ground automatic station is less than or equal to the visibility threshold is determined, which is recorded as the meteorological anomaly time period. The meteorological impact data within the meteorological anomaly time period is further obtained, and the meteorological impact index within the meteorological anomaly time period is calculated. The normal meteorological impact index range is determined based on the meteorological impact index within the meteorological anomaly time period, and the meteorological impact index after the status check is calculated. If the meteorological impact index exceeds the normal meteorological impact index range, the module will trigger the corresponding warning signal and output the meteorological anomaly report to the multi-source data assimilation module.

[0058] The multi-source data assimilation module is set in the Rth time period. At the time point when the meteorological anomaly report is received, the multi-source data is assimilated. The meteorological data forecast value output by the numerical weather forecast model is compared with the meteorological data observation value of the ground automatic station. The accuracy of the observation data is improved through data assimilation. The meteorological data forecast value, meteorological data observation value and meteorological data error are combined through the three-dimensional variational assimilation method to obtain the meteorological data that best conforms to the actual meteorological state, which is recorded as the meteorological data analysis value. The specific formula of the meteorological data analysis value is as follows:

[0059] ;

[0060] Where i represents the i-th time point, which is within the time period R, and a(i) represents the meteorological data analysis value at the i-th time point. represents the forecast value of meteorological data at the i-th time point, represents the observed value of meteorological data at the i-th time point, represents the inverse matrix of the meteorological data forecast error covariance matrix, which is obtained from the numerical weather forecast model parameters of the output meteorological data forecast value. H represents the observation operator, which is used to map the model variables from the grid space to the observation space. T represents the transposition sign, r represents the observation error, and u represents the representative error. Express The model variables are used in the grid space to obtain the theoretical values ​​at the observation points.

[0061] The quality control module implements the quality control of the visibility of the ground automatic station by performing spatial consistency check and background consistency check on the meteorological data observation values. The spatial consistency check needs to compare the data of the meteorological data observation values ​​of the checked point with those of the surrounding observation points, and adopts objective analysis methods, such as interpolation method and Barnes method. The estimated value of the checked point is calculated based on the data of the surrounding observation points. The thresholds of different data types are set according to the characteristics of the data and application requirements. The data types are distinguished by temperature, humidity, wind speed and direction. The difference between the meteorological data observation value and the forecast value of the checked point is compared with the size relationship of the given threshold. If the difference between the meteorological data observation value and the forecast value of the checked point is greater than the threshold, the meteorological data analysis value is considered to be abnormal or suspicious and has not passed the spatial consistency check. Otherwise, the meteorological data observation value is judged to have passed the spatial consistency check, thereby screening the meteorological data analysis values ​​that meet the quality requirements;

[0062] The representative error is processed by background consistency check. Within the time period R, the difference ax(i) between the observed value and the forecast value of the meteorological data at each time point and the average value of the difference are calculated. The standard deviation of the observed and predicted values ​​of the meteorological data for this period , set the meteorological data evaluation index, the specific formula is as follows:

[0063] ;

[0064] A threshold value V of the evaluation index is preset. When Pj(i) is greater than or equal to the threshold value, the observation value and forecast value of the meteorological data at that time point are removed, thereby removing the analysis value of the meteorological data at that time point, wherein the threshold value V is determined by a double weight method.

[0065] The visibility quality control system based on multi-source data to assist ground automatic stations proposed in this embodiment helps to improve the accuracy of visibility data, enhance the representativeness and comparability of visibility data, improve the efficiency of visibility data quality control, and support weather forecasting and disaster warning. It plays an important role in improving the accuracy of weather forecasts and reducing the risks of meteorological disasters.

[0066] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0067] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Based on multi-source data to assist ground automatic station visibility quality control system, it is characterized by: include: The multi-source data collection module is used to collect various meteorological data from multiple different information sources in real time, and determine the temperature state change value, humidity state change value and wind speed state change value of each time period according to the real-time observation value of the collected meteorological data; The ground automatic station meteorological anomaly judgment module is used to check the status of the meteorological observation data of the ground automatic station, and set the meteorological impact index based on the temperature state change value, humidity state change value and wind speed state change value; Using the formula represents the meteorological impact index, where x represents the x-th time period, Q(x) represents the meteorological impact index of the x-th time period, W(x) represents the temperature state change value of the x-th time period, S(x) represents the humidity state change value of the x-th time period, and F(x) represents the wind speed state change value of the x-th time period. The weight coefficient representing the change value of the temperature state, The weight coefficient representing the change value of humidity state, The weight coefficient representing the wind speed state change value; Determine the visibility threshold according to the actual requirements for the visibility of the ground automatic station. Based on the historical data of the ground automatic station, determine the time period when the visibility of the ground automatic station is less than or equal to the visibility threshold, record it as the meteorological anomaly time period, further obtain the meteorological impact data within the meteorological anomaly time period, calculate the meteorological impact index within the meteorological anomaly time period, determine the normal meteorological impact index range according to the meteorological impact index within the meteorological anomaly time period, calculate the meteorological impact index after the status check, if the meteorological impact index exceeds the normal meteorological impact index range, the module will trigger the corresponding warning signal, and output the meteorological anomaly report to the multi-source data assimilation module; The multi-source data assimilation module integrates the meteorological data forecast values ​​output by the numerical weather forecast model with the meteorological data observation values ​​of the ground automatic station, and combines the meteorological data forecast values, meteorological data observation values ​​and meteorological data errors through the three-dimensional variational assimilation method to determine the meteorological data analysis value for meteorological data that is not in a normal state; Using the formula represents the meteorological data analysis value, where i represents the i-th time point, which is within the time period R, and a(i) represents the meteorological data analysis value at the i-th time point. represents the forecast value of meteorological data at the i-th time point, represents the observed value of meteorological data at the i-th time point, represents the inverse matrix of the meteorological data forecast error covariance matrix, which is obtained from the numerical weather forecast model parameters of the output meteorological data forecast value. H represents the observation operator, which is used to map the model variables from the grid space to the observation space. T represents the transposition sign, r represents the observation error, and u represents the representative error. Express Use the model variables in the grid space to obtain the theoretical values ​​at the observation points; The quality control module screens out meteorological data that fail the spatial consistency check by performing a spatial consistency check, and screens out meteorological data that fail the background consistency check by performing a background consistency check.

2. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: Determine the temperature state change value in each time period. The specific method is: Using the formula represents the temperature state change value, where x represents the x-th time period, W(x) represents the temperature state change value of the x-th time period, w(x) represents the average temperature of the x-th time period, minw(x) represents the minimum temperature of the x-th time period, and maxw(x) represents the maximum temperature of the x-th time period.

3. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: Determine the humidity state change value for each time period. The specific method is: Using the formula Represents the humidity state change value, where x represents the x-th time period, S(x) represents the humidity state change value of the x-th time period, s(x) represents the average humidity of the x-th time period, a represents a constant, s[x(t)] represents the humidity at the end time point of the x-th time period, and s[x(0)] represents the humidity at the start time point of the x-th time period.

4. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: Determine the wind speed state change value in each time period. The specific method is: Using the formula Represents the wind speed state change value, where x represents the x-th time period, F(x) represents the wind speed state change value in the x-th time period, f(x) represents the average wind speed in the x-th time period, and p(x) represents the frequency of wind direction blowing towards the ground automatic station in the x-th time period.

5. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: Check the status of meteorological observation data of ground automatic stations. The specific methods are as follows: Sensor status check: judge the working status of the sensor equipment based on the switch status and voltage status of each sensor of the ground automatic station. When the sensor is in the on state and the working voltage is in the normal voltage range, it means that the sensor status check of the sensor has passed. Otherwise, it means that the sensor status check of the sensor has failed. Data status check: check the format and missing parts of the collected data according to the format and method requirements of the relevant organizations for the collected data, and correct the wrong format and missing parts; check the internal comparison relationship and time sequence of the data according to the basic knowledge of meteorological observation, record the data that does not meet the normal comparison relationship and time sequence as wrong data, and adjust the wrong data based on the basic knowledge of meteorological observation; Suspicious data check: according to the historical data collected by the ground automatic station under normal conditions and the climatological laws, set the limit values ​​for the data in different seasons and time periods. When the data at a certain point in time exceeds the limit value, the data in that time period is judged to be suspicious, recorded as suspicious data, and all suspicious data are deleted.

6. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: By performing a spatial consistency check, the meteorological data that fails the spatial consistency check are screened out. The specific method is as follows: The spatial consistency check needs to compare the data of the meteorological data observation values ​​of the checked point with those of the surrounding observation points. The Barnes method is used to calculate the estimated value of the checked point based on the data of the surrounding observation points, and the thresholds of different data types are set based on the characteristics of the data and application requirements. The data types are distinguished by temperature, humidity, wind speed and direction. The difference between the meteorological data observation value and the forecast value of the checked point is compared with the given threshold. If the difference between the meteorological data observation value and the forecast value of the checked point is greater than the threshold, the meteorological data analysis value is considered to be abnormal or suspicious and has not passed the spatial consistency check. Otherwise, the meteorological data observation value is judged to have passed the spatial consistency check.

7. The visibility quality control system based on multi-source data-assisted ground automatic station according to claim 1 is characterized in that: By performing a background consistency check, the meteorological data that fails the background consistency check is screened out. The specific method is as follows: The representative error is processed by background consistency check. In the time period R, the difference ax(i) between the observed value and the forecast value of the meteorological data at each time point and the average value of the difference are calculated. The standard deviation of the observed and predicted values ​​of the meteorological data for this period , set the meteorological data evaluation index, and use the formula It represents the meteorological data evaluation index, and the evaluation index threshold V is preset. When Pj(i) is greater than or equal to the threshold, the meteorological data observation value and forecast value at that time point are removed, thereby removing the meteorological data analysis value at that time point.

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